Deep Learning

Asrivat1/DeepLearningVideoGames. UFLDL Tutorial - Ufldl. From Ufldl Description: This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning.

By working through it, you will also get to implement several feature learning/deep learning algorithms, get to see them work for yourself, and learn how to apply/adapt these ideas to new problems. This tutorial assumes a basic knowledge of machine learning (specifically, familiarity with the ideas of supervised learning, logistic regression, gradient descent). If you are not familiar with these ideas, we suggest you go to this Machine Learning course and complete sections II, III, IV (up to Logistic Regression) first.

Sparse Autoencoder Vectorized implementation Preprocessing: PCA and Whitening Softmax Regression Self-Taught Learning and Unsupervised Feature Learning Building Deep Networks for Classification Linear Decoders with Autoencoders Working with Large Images Note: The sections above this line are stable. Miscellaneous Miscellaneous Topics Advanced Topics: Sparse Coding. What is Deep Learning?
Scyfer is a University of Amsterdam spinoff that specializes in deep learning technology.

We build deep neural network solutions for image and speech recognition as well as for recommender systems.
A Primer on Deep Learning. Schedule. Welcome — Theano 0.6 documentation. How to Seek Help¶ The appropriate venue for seeking help depends on the kind of question you have.

How do I? – theano-users mailing list or StackOverflowI got this error, why? – theano-users mailing list or StackOverflow (please include the full error message, even if it’s long)I got this error and I’m sure it’s a bug – Github ticketI have an idea/request – post the suggestion to theano-dev or, even better, implement the idea and submit a GitHub pull request!
Why do you? Please do take some time to search for similar questions that were asked and answered in the past.

When asking questions on StackOverflow, please use the theano tag, so your question can be found, and follow StackOverflow’s guidance on asking questions. It’s often helpful to include the following details with your question: Spending the time to create a minimal specific example of a problem is likely to get you to an answer quicker than posting something quickly that has too much irrelevant detail or is too vague.

Deep Learning. Overview — Pylearn2 dev documentation. This page gives a high-level overview of the Pylearn2 library and describes how the various parts fit together.

First, before learning Pylearn2 it is imperative that you have a good understanding of Theano. Before learning Pylearn2 you should first understand: How Theano uses Variables, Ops, and Apply nodes to represent symbolic expressions.What a Theano function is.What Theano shared variables are and how they can make state persist between calls to Theano functions. Once you have that under your belt, we can move on to Pylearn2 itself. Note that throughout this page we will mention several different classes and functions but not completely describe their parameters. The Model class A good central starting point is the Model, defined in pylearn2.models.model.py. A Model can be almost anything. Training a Model while avoiding Pylearn2 entanglements Part of the Pylearn2 vision is that users should be able to use only the pieces of the library that they want to.

In a DBM, the connections amongst the visible and hidden units have a particular structure. This structure removes connections from a fully connected model such that layers in the network can be naturally defined. In a DBM, each layer has no connections amongst its units. Each unit in a layer is connected to every unit in both the layers immediately above and immediately below. The DBM type structure is the middle one in the picture here. There are at least three good reasons for imposing this type of restriction, instead of just allowing all units to talk to all other units (this variant is called a fully connected Boltzmann Machine — that’s the one on the left in the picture): Computational tractability. Here some of the weights are explicitly shown — all four U weights (connecting the visible unit to the hidden units) and three of the W weights are explicitly shown (the bold lines with the W next to them). becomes. .
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